Identification of electrical signatures with local machine learning models on an iot-enabled monitor

A computer program product embodied in a non-transitory computer readable medium for analysis, the computer program product comprising code which causes one or more processors to perform operations of:

• pushing, by a cloud server, to an industrial IoT electrical monitor (IIEM), a machine learning (ML) model, wherein the HEM is coupled to an industrial device within an industrial environment;
• recording, by the IIEM, operational data associated with the industrial device, wherein the recording is based on a sampling frequency;
• identifying, by the ML model, one or more anomalous events associated with the industrial device, wherein the identifying is based on the operational data that was recorded and contextual information;
• reporting, by the IIEM, the one or more anomalous events that were identified;
• and
• verifying, by a user, the one or more anomalous events.

A computer system for analysis comprising:

• a memory which stores instructions;
• one or more processors attached to the memory, wherein the one or more processors, when executing the instructions which are stored, are configured to:
• push, by a cloud server, to an industrial IoT electrical monitor (IIEM), a machine learning (ML) model, wherein the HEM is coupled to an industrial device within an industrial environment;
• record, by the IIEM, operational data associated with the industrial device, wherein the recording is based on a sampling frequency;
• identify, by the ML model, one or more anomalous events associated with the industrial device, wherein the identifying is based on the operational data that was recorded and contextual information;
• report, by the IIEM, the one or more anomalous events that were identified;
• and
• verify, by a user, the one or more anomalous events.